### Loading in the Data ###
setwd("")
data<-read.csv("02_Clean Racial Shift Replication.csv")
data_prop<-read.csv("02_Clean Racial Shift Full Replication Proprietary.csv")

library(dplyr)
library(modelsummary)

#### Filtering the Data to Only Include Participants Who Passed the Factual Manipulation Check ####
data <- data %>%
  filter(
    (Treatment == "Control" & check_geo == "Recent data reveal more people moved over the last year.") |
      (Treatment == "Cultural Shift" & check_culture == "Racial minorities will be the majority of leads in TV shows and movies by the year 2032.") |
      (Treatment == "Political Shift" & check_political == "Racial minorities will be the majority of voters by the year 2064.") |
      (Treatment == "Population Shift" & check_pop == "Racial minorities will outnumber Whites in the U.S. by the year 2046.")
  )

data_prop <- data_prop %>%
  filter(
    (Treatment == "Control" & check_geo == "Recent data reveal more people moved over the last year.") |
      (Treatment == "Cultural Shift" & check_culture == "Racial minorities will be the majority of leads in TV shows and movies by the year 2032.") |
      (Treatment == "Political Shift" & check_political == "Racial minorities will be the majority of voters by the year 2064.") |
      (Treatment == "Population Shift" & check_pop == "Racial minorities will outnumber Whites in the U.S. by the year 2046.")
  )


# Average Effects ---------------------------------------------------------
SMC_ND_reg1<-lm(trends_closed_demog~ Treatment, data= data_prop)
SMC_ND_reg2<-lm(trends_closed_pol~ Treatment, data= data_prop)
SMC_ND_reg3<-lm(trends_closed_media~ Treatment, data= data_prop)

SMC_reg1<-lm(trends_closed_demog~ Treatment, data= data)
SMC_reg2<-lm(trends_closed_pol~ Treatment, data= data)
SMC_reg3<-lm(trends_closed_media~ Treatment, data= data)

### Status Change ###
SC1_nd <-lm(SC_scale~ Treatment, data= data_prop)
SC1 <-lm(SC_scale~ Treatment, data= data)

### Psychological Mechanisms ###
# symbolic threat
Scale_ND_reg1<-lm(ST_scale~ Treatment, data= data_prop)
Scale_reg1<-lm(ST_scale~ Treatment, data= data)

# realistic threat
Scale_ND_reg2<-lm(RT_scale~ Treatment, data= data_prop)
Scale_reg2<-lm(RT_scale~ Treatment, data= data)

# prototypicality threat
Scale_ND_reg3<-lm(PT_scale_full~ Treatment, data= data_prop)
Scale_reg3<-lm(PT_scale_full~ Treatment, data= data)

### Group Attitudes ###
# views of black americans
FT_ND_reg1<-lm(Black_FT~ Treatment, data= data_prop)
FT_reg1<-lm(Black_FT~ Treatment, data= data)

# views of latino americans
FT_ND_reg2<-lm(Latinos_FT~ Treatment, data= data_prop)
FT_reg2<-lm(Latinos_FT~ Treatment, data= data)

# views of asian americans
FT_ND_reg3<-lm(Asian_FT~ Treatment, data= data_prop)
FT_reg3<-lm(Asian_FT~ Treatment, data= data)

# views of white americans
FT_ND_reg4<-lm(White_FT~ Treatment, data= data_prop)
FT_reg4<-lm(White_FT~ Treatment, data= data)

# views of blm
FT_ND_reg5<-lm(BLM_FT~ Treatment, data= data_prop)
FT_reg5<-lm(BLM_FT~ Treatment, data= data)

# views of the alt-right
FT_ND_reg6<-lm(Alt_Right_FT~ Treatment, data= data_prop)
FT_reg6<-lm(Alt_Right_FT~ Treatment, data= data)


### Policy Positions ###
# racial policy
Pol_ND_reg1<-lm(racialpolicy_scale~ Treatment, data= data_prop)
Pol_reg1<-lm(racialpolicy_scale~ Treatment, data= data)

# non racial policy
Pol_ND_reg2<-lm(nonracialpolicy_scale~ Treatment, data= data_prop)
Pol_reg2<-lm(nonracialpolicy_scale~ Treatment, data= data)

### Participation ###
# political backlash participation
Part_ND_reg1<-lm(backlash_pol~ Treatment, data= data_prop)
Part_reg1<-lm(backlash_pol~ Treatment, data= data)

# personal backlash participation
Part_ND_reg2<-lm(backlash_person~ Treatment, data= data_prop)
Part_reg2<-lm(backlash_person~ Treatment, data= data)

# political supportive participation
Part_ND_reg3<-lm(support_pol~ Treatment, data= data_prop)
Part_reg3<-lm(support_pol~ Treatment, data= data)

# personal supportive participation
Part_ND_reg4<-lm(support_person~ Treatment, data= data_prop)
Part_reg4<-lm(support_person~ Treatment, data= data)



# Ideology Moderation -----------------------------------------------------
SMC_ND_reg1C<-lm(trends_closed_demog~ Treatment*cons_ideo_scale, data= data_prop)
SMC_ND_reg2C<-lm(trends_closed_pol~ Treatment*cons_ideo_scale, data= data_prop)
SMC_ND_reg3C<-lm(trends_closed_media~ Treatment*cons_ideo_scale, data= data_prop)

SMC_reg1C<-lm(trends_closed_demog~ Treatment*cons_ideo_scale, data= data)
SMC_reg2C<-lm(trends_closed_pol~ Treatment*cons_ideo_scale, data= data)
SMC_reg3C<-lm(trends_closed_media~ Treatment*cons_ideo_scale, data= data)

### Status Change ###
SC1_nd_C <-lm(SC_scale~ Treatment*cons_ideo_scale, data= data_prop)
SC1_C <-lm(SC_scale~ Treatment*cons_ideo_scale, data= data)

### Psychological Mechanisms ###
# symbolic threat
Scale_ND_reg1C<-lm(ST_scale~ Treatment*cons_ideo_scale, data= data_prop)
Scale_reg1C<-lm(ST_scale~ Treatment*cons_ideo_scale, data= data)

# realistic threat
Scale_ND_reg2C<-lm(RT_scale~ Treatment*cons_ideo_scale, data= data_prop)
Scale_reg2C<-lm(RT_scale~ Treatment*cons_ideo_scale, data= data)

# prototypicality threat
Scale_ND_reg3C<-lm(PT_scale_full~ Treatment*cons_ideo_scale, data= data_prop)
Scale_reg3C<-lm(PT_scale_full~ Treatment*cons_ideo_scale, data= data)

### Group Attitudes ###
# views of black americans
FT_ND_reg1C<-lm(Black_FT~ Treatment*cons_ideo_scale, data= data_prop)
FT_reg1C<-lm(Black_FT~ Treatment*cons_ideo_scale, data= data)

# views of latino americans
FT_ND_reg2C<-lm(Latinos_FT~ Treatment*cons_ideo_scale, data= data_prop)
FT_reg2C<-lm(Latinos_FT~ Treatment*cons_ideo_scale, data= data)

# views of asian americans
FT_ND_reg3C<-lm(Asian_FT~ Treatment*cons_ideo_scale, data= data_prop)
FT_reg3C<-lm(Asian_FT~ Treatment*cons_ideo_scale, data= data)

# views of white americans
FT_ND_reg4C<-lm(White_FT~ Treatment*cons_ideo_scale, data= data_prop)
FT_reg4C<-lm(White_FT~ Treatment*cons_ideo_scale, data= data)

# views of blm
FT_ND_reg5C<-lm(BLM_FT~ Treatment*cons_ideo_scale, data= data_prop)
FT_reg5C<-lm(BLM_FT~ Treatment*cons_ideo_scale, data= data)

# views of the alt-right
FT_ND_reg6C<-lm(Alt_Right_FT~ Treatment*cons_ideo_scale, data= data_prop)
FT_reg6C<-lm(Alt_Right_FT~ Treatment*cons_ideo_scale, data= data)


### Policy Positions ###
# racial policy
Pol_ND_reg1C<-lm(racialpolicy_scale~ Treatment*cons_ideo_scale, data= data_prop)
Pol_reg1C<-lm(racialpolicy_scale~ Treatment*cons_ideo_scale, data= data)

# non racial policy
Pol_ND_reg2C<-lm(nonracialpolicy_scale~ Treatment*cons_ideo_scale, data= data_prop)
Pol_reg2C<-lm(nonracialpolicy_scale~ Treatment*cons_ideo_scale, data= data)

### Participation ###
# political backlash participation
Part_ND_reg1C<-lm(backlash_pol~ Treatment*cons_ideo_scale, data= data_prop)
Part_reg1C<-lm(backlash_pol~ Treatment*cons_ideo_scale, data= data)

# personal backlash participation
Part_ND_reg2C<-lm(backlash_person~ Treatment*cons_ideo_scale, data= data_prop)
Part_reg2C<-lm(backlash_person~ Treatment*cons_ideo_scale, data= data)

# political supportive participation
Part_ND_reg3C<-lm(support_pol~ Treatment*cons_ideo_scale, data= data_prop)
Part_reg3C<-lm(support_pol~ Treatment*cons_ideo_scale, data= data)

# personal supportive participation
Part_ND_reg4C<-lm(support_person~ Treatment*cons_ideo_scale, data= data_prop)
Part_reg4C<-lm(support_person~ Treatment*cons_ideo_scale, data= data)

# Partisanship Moderation -------------------------------------------------
SMC_ND_reg1P<-lm(trends_closed_demog~ Treatment*political_party_preference, data= data_prop)
SMC_ND_reg2P<-lm(trends_closed_pol~ Treatment*political_party_preference, data= data_prop)
SMC_ND_reg3P<-lm(trends_closed_media~ Treatment*political_party_preference, data= data_prop)

SMC_reg1P<-lm(trends_closed_demog~ Treatment*political_party_preference, data= data)
SMC_reg2P<-lm(trends_closed_pol~ Treatment*political_party_preference, data= data)
SMC_reg3P<-lm(trends_closed_media~ Treatment*political_party_preference, data= data)

### Status Change ###
SC1_nd_P <-lm(SC_scale~ Treatment*political_party_preference, data= data_prop)
SC1_P <-lm(SC_scale~ Treatment*political_party_preference, data= data)

### Psychological Mechanisms ###
# symbolic threat
Scale_ND_reg1P<-lm(ST_scale~ Treatment*political_party_preference, data= data_prop)
Scale_reg1P<-lm(ST_scale~ Treatment*political_party_preference, data= data)

# realistic threat
Scale_ND_reg2P<-lm(RT_scale~ Treatment*political_party_preference, data= data_prop)
Scale_reg2P<-lm(RT_scale~ Treatment*political_party_preference, data= data)

# prototypicality threat
Scale_ND_reg3P<-lm(PT_scale_full~ Treatment*political_party_preference, data= data_prop)
Scale_reg3P<-lm(PT_scale_full~ Treatment*political_party_preference, data= data)

### Group Attitudes ###
# views of black americans
FT_ND_reg1P<-lm(Black_FT~ Treatment*political_party_preference, data= data_prop)
FT_reg1P<-lm(Black_FT~ Treatment*political_party_preference, data= data)

# views of latino americans
FT_ND_reg2P<-lm(Latinos_FT~ Treatment*political_party_preference, data= data_prop)
FT_reg2P<-lm(Latinos_FT~ Treatment*political_party_preference, data= data)

# views of asian americans
FT_ND_reg3P<-lm(Asian_FT~ Treatment*political_party_preference, data= data_prop)
FT_reg3P<-lm(Asian_FT~ Treatment*political_party_preference, data= data)

# views of white americans
FT_ND_reg4P<-lm(White_FT~ Treatment*political_party_preference, data= data_prop)
FT_reg4P<-lm(White_FT~ Treatment*political_party_preference, data= data)

# views of blm
FT_ND_reg5P<-lm(BLM_FT~ Treatment*political_party_preference, data= data_prop)
FT_reg5P<-lm(BLM_FT~ Treatment*political_party_preference, data= data)

# views of the alt-right
FT_ND_reg6P<-lm(Alt_Right_FT~ Treatment*political_party_preference, data= data_prop)
FT_reg6P<-lm(Alt_Right_FT~ Treatment*political_party_preference, data= data)


### Policy Positions ###
# racial policy
Pol_ND_reg1P<-lm(racialpolicy_scale~ Treatment*political_party_preference, data= data_prop)
Pol_reg1P<-lm(racialpolicy_scale~ Treatment*political_party_preference, data= data)

# non racial policy
Pol_ND_reg2P<-lm(nonracialpolicy_scale~ Treatment*political_party_preference, data= data_prop)
Pol_reg2P<-lm(nonracialpolicy_scale~ Treatment*political_party_preference, data= data)

### Participation ###
# political backlash participation
Part_ND_reg1P<-lm(backlash_pol~ Treatment*political_party_preference, data= data_prop)
Part_reg1P<-lm(backlash_pol~ Treatment*political_party_preference, data= data)

# personal backlash participation
Part_ND_reg2P<-lm(backlash_person~ Treatment*political_party_preference, data= data_prop)
Part_reg2P<-lm(backlash_person~ Treatment*political_party_preference, data= data)

# political supportive participation
Part_ND_reg3P<-lm(support_pol~ Treatment*political_party_preference, data= data_prop)
Part_reg3P<-lm(support_pol~ Treatment*political_party_preference, data= data)

# personal supportive participation
Part_ND_reg4P<-lm(support_person~ Treatment*political_party_preference, data= data_prop)
Part_reg4P<-lm(support_person~ Treatment*political_party_preference, data= data)


# Tables -----------------------------------------------------------

# * Average Effects -------------------------------------------------------
coefs <- c("TreatmentCultural Shift" = "Cultural Shift Treatment", 
           "TreatmentPolitical Shift" = "Political Shift Treatment", 
           "TreatmentPopulation Shift" = "Population Shift Treatment",
           "(Intercept)" = "Constant")


modelsummary(list("Demographic Minority - Full Sample" = SMC_ND_reg1, "Demographic Minority - Retained" = SMC_reg1,
                  "Political Minority- Full Sample" = SMC_ND_reg2, "Political Minority - Retained" = SMC_reg2,
                  "Cultural Minority - Full Sample" = SMC_ND_reg3, " Cultural Minority - Retained" = SMC_reg3),
             title = "Subjective Manipulation Checks. Whites soon a...",
             # output = 'tables/manip_checks_fmc.docx',
             stars = c('*' = .05), 
             coef_map = coefs,
             vcov = c("HC1"),
             notes = list('OLS coefficients with standard errors in parentheses. Variables scaled 0-1.'),
             gof_map = c("nobs", "r.squared"))


modelsummary(list("Status Change - Full Sample" = SC1_nd, "Status Change - Retained" = SC1,
                  "Symbolic Threat - Full Sample" = Scale_ND_reg1, "Symbolic Threat - Retained" = Scale_reg1,
                  "Realistic Threat - Full Sample" = Scale_ND_reg2, "Realistic Threat - Retained" = Scale_reg2,
                  "Prototypicality Threat - Full Sample" = Scale_ND_reg3, "Prototypicality Threat - Retained" = Scale_reg3
),
title = "Mechanisms",
# output = 'tables/mechanisms_fmc.docx',
stars = c('*' = .05), 
coef_map = coefs,
vcov = c("HC1"),
notes = list('OLS coefficients with standard errors in parentheses. Variables scaled 0-1.'),
gof_map = c("nobs", "r.squared"))


modelsummary(list("Racial Policy Scale - Full Sample" = Pol_ND_reg1, "Racial Policy Scale - Retained" = Pol_reg1,
                  "Nonracial Policy Scale - Full Sample" = Pol_ND_reg2, "Nonracial Policy Scale - Retained" = Pol_reg2
),
title = "Policy Attitudes",
# output = 'tables/policy_fmc.docx',
stars = c('*' = .05), 
coef_map = coefs,
vcov = c("HC1"),
notes = list('OLS coefficients with standard errors in parentheses. Variables scaled 0-1.'),
gof_map = c("nobs", "r.squared"))


modelsummary(list("Black FT - Full Sample" = FT_ND_reg1, "Black FT - Retained" = FT_reg1,
                  "Latino FT - Full Sample" = FT_ND_reg2, "Latino FT - Retained" = FT_reg2,
                  "Asian FT - Full Sample" = FT_ND_reg3, "Asian FT - Retained" = FT_reg3,
                  "BLM FT - Full Sample" = FT_ND_reg5, "BLM FT - Retained" = FT_reg5
),
title = "Evaluations of Non-White Groups",
# output = 'tables/ft_nonwhite_fmc.docx',
stars = c('*' = .05), 
coef_map = coefs,
vcov = c("HC1"),
notes = list('OLS coefficients with standard errors in parentheses. Variables scaled 0-1.'),
gof_map = c("nobs", "r.squared"))

modelsummary(list("White FT - Full Sample" = FT_ND_reg4, "White FT - Retained" = FT_reg4,
                  "Alt-Right FT - Full Sample" = FT_ND_reg6, "Alt-Right FT - Retained" = FT_reg6
),
title = "Evaluations of White Groups",
# output = 'tables/ft_white_fmc.docx',
stars = c('*' = .05), 
coef_map = coefs,
vcov = c("HC1"),
notes = list('OLS coefficients with standard errors in parentheses. Variables scaled 0-1.'),
gof_map = c("nobs", "r.squared"))

modelsummary(list("Political Backlash - Full Sample" = Part_ND_reg1, "Political Backlash - Retained" = Part_reg1,
                  "Personal Backlash - Full Sample" = Part_ND_reg2, "Personal Backlash - Retained" = Part_reg2,
                  "Political Supportive - Full Sample" = Part_ND_reg3, "Political Supportive - Retained" = Part_reg3,
                  "Personal Supportive - Full Sample" = Part_ND_reg4, "Personal Supportive - Retained" = Part_reg4
),
title = "Participatory Intentions",
# output = 'tables/particip_fmc.docx',
stars = c('*' = .05), 
coef_map = coefs,
vcov = c("HC1"),
notes = list('OLS coefficients with standard errors in parentheses. Variables scaled 0-1.'),
gof_map = c("nobs", "r.squared"))



# * Ideology ----------------------------------------------------------------
coefs <- c("TreatmentCultural Shift" = "Cultural Shift Treatment",
           "TreatmentCultural Shift:cons_ideo_scale" = "Culture*Ideology",
           "TreatmentPolitical Shift" = "Political Shift Treatment",
           "TreatmentPolitical Shift:cons_ideo_scale" = "Political*Ideology",
           "TreatmentPopulation Shift" = "Population Shift Treatment",
           "TreatmentPopulation Shift:cons_ideo_scale" = "Pop*Ideology",
           "cons_ideo_scale" = "Ideology",
           "(Intercept)" = "Constant")


modelsummary(list("Status Change - Full Sample" = SC1_nd_C, "Status Change - Retained" = SC1_C,
                  "Symbolic Threat - Full Sample" = Scale_ND_reg1C, "Symbolic Threat - Retained" = Scale_reg1C,
                  "Realistic Threat - Full Sample" = Scale_ND_reg2C, "Realistic Threat - Retained" = Scale_reg2C,
                  "Prototypicality Threat - Full Sample" = Scale_ND_reg3C, "Prototypicality Threat - Retained" = Scale_reg3C
),
title = "Mechanisms",
# output = 'tables/ideo_mechanisms_fmc.docx',
stars = c('*' = .05), 
coef_map = coefs,
vcov = c("HC1"),
notes = list('OLS coefficients with standard errors in parentheses. Variables scaled 0-1.'),
gof_map = c("nobs", "r.squared"))


modelsummary(list("Racial Policy Scale - Full Sample" = Pol_ND_reg1C, "Racial Policy Scale - Retained" = Pol_reg1C,
                  "Nonracial Policy Scale - Full Sample" = Pol_ND_reg2C, "Nonracial Policy Scale - Retained" = Pol_reg2C
),
title = "Policy Attitudes",
# output = 'tables/ideo_policy_fmc.docx',
stars = c('*' = .05), 
coef_map = coefs,
vcov = c("HC1"),
notes = list('OLS coefficients with standard errors in parentheses. Variables scaled 0-1.'),
gof_map = c("nobs", "r.squared"))


modelsummary(list("Black FT - Full Sample" = FT_ND_reg1C, "Black FT - Retained" = FT_reg1C,
                  "Latino FT - Full Sample" = FT_ND_reg2C, "Latino FT - Retained" = FT_reg2C,
                  "Asian FT - Full Sample" = FT_ND_reg3C, "Asian FT - Retained" = FT_reg3C,
                  "BLM FT - Full Sample" = FT_ND_reg5C, "BLM FT - Retained" = FT_reg5C
),
title = "Evaluations of Non-White Groups",
# output = 'tables/ideo_ft_nonwhite_fmc.docx',
stars = c('*' = .05), 
coef_map = coefs,
vcov = c("HC1"),
notes = list('OLS coefficients with standard errors in parentheses. Variables scaled 0-1.'),
gof_map = c("nobs", "r.squared"))

modelsummary(list("White FT - Full Sample" = FT_ND_reg4C, "White FT - Retained" = FT_reg4C,
                  "Alt-Right FT - Full Sample" = FT_ND_reg6C, "Alt-Right FT - Retained" = FT_reg6C
),
title = "Evaluations of White Groups",
# output = 'tables/ideo_ft_white_fmc.docx',
stars = c('*' = .05), 
coef_map = coefs,
vcov = c("HC1"),
notes = list('OLS coefficients with standard errors in parentheses. Variables scaled 0-1.'),
gof_map = c("nobs", "r.squared"))

modelsummary(list("Political Backlash - Full Sample" = Part_ND_reg1C, "Political Backlash - Retained" = Part_reg1C,
                  "Personal Backlash - Full Sample" = Part_ND_reg2C, "Personal Backlash - Retained" = Part_reg2C,
                  "Political Supportive - Full Sample" = Part_ND_reg3C, "Political Supportive - Retained" = Part_reg3C,
                  "Personal Supportive - Full Sample" = Part_ND_reg4C, "Personal Supportive - Retained" = Part_reg4C
),
title = "Participatory Intentions",
# output = 'tables/ideo_particip_fmc.docx',
stars = c('*' = .05), 
coef_map = coefs,
vcov = c("HC1"),
notes = list('OLS coefficients with standard errors in parentheses. Variables scaled 0-1.'),
gof_map = c("nobs", "r.squared"))


# * Partisanship ----------------------------------------------------------------
coefs <- c("TreatmentCultural Shift" = "Cultural Shift Treatment",
           "TreatmentCultural Shift:political_party_preference" = "Culture*PID",
           "TreatmentPolitical Shift" = "Political Shift Treatment",
           "TreatmentPolitical Shift:political_party_preference" = "Political*PID",
           "TreatmentPopulation Shift" = "Population Shift Treatment",
           "TreatmentPopulation Shift:political_party_preference" = "Pop*PID",
           "political_party_preference" = "Partisanship",
           "(Intercept)" = "Constant")


modelsummary(list("Status Change - Full Sample" = SC1_nd_P, "Status Change - Retained" = SC1_P,
                  "Symbolic Threat - Full Sample" = Scale_ND_reg1P, "Symbolic Threat - Retained" = Scale_reg1P,
                  "Realistic Threat - Full Sample" = Scale_ND_reg2P, "Realistic Threat - Retained" = Scale_reg2P,
                  "Prototypicality Threat - Full Sample" = Scale_ND_reg3P, "Prototypicality Threat - Retained" = Scale_reg3P
),
title = "Mechanisms",
# output = 'tables/pid_mechanisms_fmc.docx',
stars = c('*' = .05), 
coef_map = coefs,
vcov = c("HC1"),
notes = list('OLS coefficients with standard errors in parentheses. Variables scaled 0-1.'),
gof_map = c("nobs", "r.squared"))


modelsummary(list("Racial Policy Scale - Full Sample" = Pol_ND_reg1P, "Racial Policy Scale - Retained" = Pol_reg1P,
                  "Nonracial Policy Scale - Full Sample" = Pol_ND_reg2P, "Nonracial Policy Scale - Retained" = Pol_reg2P
),
title = "Policy Attitudes",
# output = 'tables/pid_policy_fmc.docx',
stars = c('*' = .05), 
coef_map = coefs,
vcov = c("HC1"),
notes = list('OLS coefficients with standard errors in parentheses. Variables scaled 0-1.'),
gof_map = c("nobs", "r.squared"))


modelsummary(list("Black FT - Full Sample" = FT_ND_reg1P, "Black FT - Retained" = FT_reg1P,
                  "Latino FT - Full Sample" = FT_ND_reg2P, "Latino FT - Retained" = FT_reg2P,
                  "Asian FT - Full Sample" = FT_ND_reg3P, "Asian FT - Retained" = FT_reg3P,
                  "BLM FT - Full Sample" = FT_ND_reg5P, "BLM FT - Retained" = FT_reg5P
),
title = "Evaluations of Non-White Groups",
# output = 'tables/pid_ft_nonwhite_fmc.docx',
stars = c('*' = .05), 
coef_map = coefs,
vcov = c("HC1"),
notes = list('OLS coefficients with standard errors in parentheses. Variables scaled 0-1.'),
gof_map = c("nobs", "r.squared"))

modelsummary(list("White FT - Full Sample" = FT_ND_reg4P, "White FT - Retained" = FT_reg4P,
                  "Alt-Right FT - Full Sample" = FT_ND_reg6P, "Alt-Right FT - Retained" = FT_reg6P
),
title = "Evaluations of White Groups",
# output = 'tables/pid_ft_white_fmc.docx',
stars = c('*' = .05), 
coef_map = coefs,
vcov = c("HC1"),
notes = list('OLS coefficients with standard errors in parentheses. Variables scaled 0-1.'),
gof_map = c("nobs", "r.squared"))

modelsummary(list("Political Backlash - Full Sample" = Part_ND_reg1P, "Political Backlash - Retained" = Part_reg1P,
                  "Personal Backlash - Full Sample" = Part_ND_reg2P, "Personal Backlash - Retained" = Part_reg2P,
                  "Political Supportive - Full Sample" = Part_ND_reg3P, "Political Supportive - Retained" = Part_reg3P,
                  "Personal Supportive - Full Sample" = Part_ND_reg4P, "Personal Supportive - Retained" = Part_reg4P
),
title = "Participatory Intentions",
# output = 'tables/pid_particip_fmc.docx',
stars = c('*' = .05), 
coef_map = coefs,
vcov = c("HC1"),
notes = list('OLS coefficients with standard errors in parentheses. Variables scaled 0-1.'),
gof_map = c("nobs", "r.squared"))

